Migrate Demographic Group For Fair GNNs

arXiv:2306.04212v2 Announce Type: replace
Abstract: Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanilla GNNs, causing biased results toward particular demographic groups (divided by sensitive attributes, such as race and age). There have been efforts to address the fairness issue. However, existing fair techniques generally divide the demographic groups by raw …

abstract arxiv cs.cy cs.lg data designing fair fairness gnns graph graph learning graph neural networks however information networks neural networks performance results training training data type